Information centric network distributed search with approximate cache

ABSTRACT

Systems and techniques for an information centric network (ICN) distributed search with approximate cache and forwarding information lookup. For example, a search interest packet may be received. Here, the search interest packet includes search criteria and a signal indicating that it is a search interest packet. A search for content—including content in a local content store—that meets the search criteria may then be performed. Once complete, a data packet that includes the results of the search may be transmitted towards an author of the search interest packet.

TECHNICAL FIELD

Embodiments described herein generally relate to computer networking andmore specifically to an information centric network (ICN) distributedsearch with approximate cache and forwarding information lookup.

BACKGROUND

ICN is an umbrella term for a new networking paradigm in whichinformation itself is named and requested from the network instead ofhosts (e.g., machines that provide information). To get content, adevice requests named content from the network itself. The contentrequest may be called an interest and transmitted via an interestpacket. As the interest packet traverses network devices (e.g.,routers), a record of the interest is kept. When a device that hascontent matching the name in the interest is encountered, that devicemay send a data packet in response to the interest packet. Typically,the data packet is tracked back through the network to the source byfollowing the traces of the interest left in the network devices.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. Like numerals havingdifferent letter suffixes may represent different instances of similarcomponents. The drawings illustrate generally, by way of example, butnot by way of limitation, various embodiments discussed in the presentdocument.

FIG. 1 illustrates an example of a distributed search engine, accordingto an embodiment.

FIG. 2 is an example of an interest packet forwarding technique,according to an embodiment.

FIG. 3 illustrates an example of a network topology for distributedsearching, according to an embodiment.

FIGS. 4A-4B illustrate an example of a message flow during execution ofa distributed search, according to an embodiment.

FIG. 5 is an example of a method for ICN distributed search withapproximate cache and forwarding information lookup, according to anembodiment.

FIG. 6 illustrates an example ICN, according to an embodiment.

FIG. 7 is a block diagram illustrating an example of a machine uponwhich one or more embodiments may be implemented.

DETAILED DESCRIPTION

Devices equipped with computing, communication, and storage resources(e.g., smart or intelligent devices) are proliferating, findingthemselves in a greater number of environments than ever before. Forexample, self-driving cars carry data center-like compute resources andcommunicate with both infrastructure components and neighboring cars toensure efficient and safe navigation on the road. Passengers ofself-driving cars also may share information—e.g., contents such asaudio streams, deals, documents, etc.—with others while on the road.

With capabilities now resident in smart devices, self-organization intoad hoc networks is becoming a more likely and useful option than it hasbeen previously. Here, devices simultaneously may be producers,processors, or consumers of different information. In such networks, ICNis a natural fit for propagating information in such a dynamicenvironment. The produced or created data, however, may be representedwith different naming conventions, syntactical structure, or synonymouswords. Because ICN relies on names for its operation, it is importantthat the network understand the semantics of names representing data toeffectively handle the information.

Traditional ICN frameworks such as named data networking (NDN) orcontent centric networking (CCN) use exact matching of an interest namewith the name of data stored into content store and lack semanticunderstanding of names. While incorporating semantic understanding intothe ICN stack may provide users with a group of similar informationfaster, it is less efficient in terms of compute and storage needed toenable semantic matching. Hence, an efficient semantic matchingtechnique that is integrated within the ICN framework would bebeneficial for searching and retrieving content.

Fuzzy Interest Forwarding (FIF) is a technique that adds a semanticunderstanding function to NDN for cache and forward information base(FIB) lookup. FIF constructs a vector space model of NDN names and thenapplies a standard vector distance function to measure similarity amongnames. Thus, if a data name is within a threshold distance from aninterest name, the data may be matched and returned in response to theinterest. An issue with FIF involves the semantic similarity functionrequiring a globally synchronized model for all nodes. Further, FIF alsotends to place a high demand on storage and computation resources.

To address the issues noted above, an efficient mechanism to findsemantically similar contents from an ICN content store and dataproducers is described herein. A naming scheme may be used in which thename of an interest packet contains one or more of: a prefix thatindicates a request for an approximate search; a search string (e.g., innatural language); a similarity score threshold (t); or a maximum numberof data entries (N) to retrieve in response to an interest. The ICN mayalso be augmented with two inverted indices that are calculated for thecontent store and FIB. The inverted indices may be updated periodicallyor when the content store or FIB entries are updated.

In operation, when the ICN layer of a node receives an interest packet,the node extracts the search string from the name (e.g., in response toparsing the prefix requesting an approximate match) and uses an invertedindex for the content store to determine whether there are entries withsimilarity scores that meet (e.g., is greater than or less thandepending on the metric) the given threshold t. If there are N entriesfound in the content store, they will be returned immediately. If thereare only K—where K<N—entries found in the content store, the invertedindex for the FIB is consulted to determine potential next hops that mayprovide semantically relevant information for the remaining (R) entries,where R=N−K. In this way, the interest may be partially filled at an ICNnode and the remaining data sought at other ICN nodes. A forwardingstrategy to choose a number of next hops out of all the hops determinedfrom FIB inverted index and a content aggregation strategy to bundlemultiple data or manifests (e.g., links, references, names, etc. toactual data) are described below.

These techniques pro-actively maintain inverted index tables for thecontent store and the FIB in each ICN node. This enables fasterinformation location and retrieval. Additional details and examples aregiven below.

FIG. 1 illustrates an example of a distributed search engine, accordingto an embodiment. The search engine will generally be part of an ICNnode (e.g., router). As illustrated, the search engine includes a queryrepresentation and generation component 105, a query partitioningcomponent 110, a content name retrieval component 115, and a contentaggregation component 130. Each of these components is implemented incircuitry, such as a processor, as described below with respect to FIG.7.

The query representation and generation component 105 is arranged tomanipulate and process a query for content names based on a convention.In an example, a search query may be signaled (e.g., represented) via aspecial interest packet. In an example, the search interest packetincludes a name field and a parameters field. In an example, the searchinterest packet includes a stop-lists field.

In an example, the name field takes the form of“/search/query=<QUERY_STRING>/entries=N,” where “/search” that is apre-defined prefix to initiate a distributed search request, althoughany prefix defined to signal a search may be used. The <QUERY_STRING>component is the search string. The “entries=N” element of the nameindicates a maximum number of matched entries requested in the interestpacket.

The parameters field may include a “similarityScoreThreshold” elementthat is used to indicate how precise the name matching needs to be topair data with the interest packet. Thus, if a comparison techniqueresults in small numbers indicate closeness between two names, then thesimilarityScoreThreshold defines the value under which such a comparisonresults in “matched” names. In an example, the parameters may includethe additional parameters (e.g., flags) “index_only” or “meta_included”to respectively indicate whether the interest packet author isrequesting links or manifests of matched data (e.g., only names, not thecontent) or whether meta information should be within the search scopeor not.

The stop-lists field may be used when a query is modified to forwardonto another network node to find entries that are not found locally.The stop-lists field may include a set of digests calculated fromalready matched content names. This helps to avoid retrieval ofduplicate content or name. In an example, the stop lists may includeother content identifiers—such as universally unique identifiers(UUIDs), Object IDs (OIDs), World Wide Web Consortium (W3C)Decentralized IDs (DIDs), etc.—or the stop lists may contain a manifestof content references—such as an Hypertext Markup Language (HTML)reference (HREF), NDN links etc.—to content that is already known to thequery issuer (e.g., the node processing the search interest packet orthe search interest packet author).

In an example, intermediate query results not found in a stop list maybe added to the stop list before proceeding to a next node. Duplicatecontents may be thus detected and omitted from the query resultsprovided by subsequent nodes. In an example, the stop list may bereturned to the originator where the delta stop list shows the newlyfound references.

In an example, similarity scores of already matched entries are alsoincluded in the stop lists. This helps to find or retrieve higherquality results in scenarios where one or more subsequent nodes (e.g.,next hops) have contents with a better similarity score than the presentnode.

The content name retrieval component 115 is arranged to produce the mostrelevant content names in response to a search query. The content nameretrieval component 115 is configured to parse the query and find localcache entries for the given search string—and, for example, using asimilarity score in the query to determine a content match. In anexample, the content name retrieval component 115 is configured to usecontent store inverted index 120 to collect the list of potentialmatches.

The content name retrieval component 115 is also configured to compute asimilarity metric between the query and content name strings. Thesimilarity score is used to rank matched entries and pick the top Nentries that have a similarity score better (greater than or less thanaccording to the metric) than the threshold specified in the searchinterest packet. In an example, the content name retrieval component 115may be implemented as a simple matching mechanism to provide a quickresponse. However, in an example, the content name retrieval component115 may be configured to use more complicated query techniques, such asterm expansion, to improve results.

The query partitioning component 110 is arranged to create a subquery ifthe number of expected results specified in the search interest packetare not found in the node's content store. The query partitioningcomponent 110 is configured to use a FIB inverted index 125—constructedfrom FIB prefixes—to find out potential routes (e.g., next hops) thatmay provide additional content names to meet the number of resultsrequested in the search interest packet. Thus, if the original searchinterest packet requests N matched entries, and there are only K entrieslocally—again where K<N—, the FIB inverted index 125 is consulted todetermine potential next hops that may provide semantically relevantinformation for the remaining entries R, where R=N−K. To accomplishthis, the query partitioning component 110 is configured to create a newsearch query with a digest of already found content names—to avoidretrieval of duplicate information—and a request for

$\frac{R}{X}$

entries. Because partial matching with FIB prefixes may result intomultiple next hop entries (referred to here as Y), X is a pre-configuredparameter—where 1≤X≤Y—that indicates how many entries of R should besatisfied by any given next hop. X is a limiting element because, ifX>1, then more than R entries may be received when the search isforwarded to more than one other node. This may result in discardingreturned results, wasting network resources. Here, X may be tuned tomanage the trade-off between search response time and network overhead(e.g., due to transmission of additional matched entries). FIG. 2,discussed below, illustrates an example of the working principle of thequery partitioning component 110 with an interest packet forwardingmechanism for a search string query, where X=1.

In an example, in a clustered network, the cluster head may act topartition the query. In such a network, search interest packets may beforwarded towards cluster heads or locally known pre-designated nodes.When these nodes have better awareness of contents in a neighborhood ofnodes, partitioning the query may be done more efficiently. For example,rather than a node selecting locally available K entries based only onsimilarity score threshold, a cluster head or designated node may helpin getting better entries with higher similarity scores within theneighborhood.

The content aggregation component 130 is arranged to receive multiplecontent responses or manifests (e.g., containing a set of names) andaggregate them together before sending them back towards the consumer(e.g., following the PIT entry for the search interest packet). Thecontent aggregation component 130 may be configured to remove the PITentry only after N data packets are sent back to the search interestpacket author.

In an example, if there are only K found in the node's content store,the content aggregation component 130 does not immediately return thoseentries to the consumer, but rather is configured to invoke the querypartitioning component 110 to perform additional queries until N entriesare found or, in an example, a pre-configured timer expires. The contentaggregation component 130 is configured to create an addition virtualface, Face_(Search), to store intermediate search results and aggregatethem with incoming results. Face_(Search) interfaces with the processperforming the aggregation. FIG. 3 illustrates an example topology ofthis partitioned query and data aggregation. FIG. 4 illustrates anexample of a message flow for this process.

The inverted indexes 120 and 125 include a vocabulary (e.g., list ofterms) and posting lists for each of the terms. A posting list may be alist of documents and locations within a document where thecorresponding term occurs. When content is received, node parses adescription of the content from the name of the content (e.g., anyparsing function that extracts a description of the content from itsname). The parsed content description then may be added to the index. Inan example, the content name is partitioned into terms. The terms may besearched for in the vocabulary list of the index and, if found, an itemwith the location (e.g., position of the term within whole content) andcontent id (e.g., a locally generated unique identifier to denote thecontent) are added into the corresponding posting list. If the terms arenot available, then a decision is made as to whether they should beadded to the vocabulary or not.

In an example, while building the vocabulary list, semantic tags ofcontent may be maintained if semantic entailment of contents isavailable. W3C Web Ontology Language (OWL) or Resource DescriptionFramework (RDF) are two examples of semantic tagging standards that maybe used. Semantic tagged values may be matched against a query thatspecifies an OWL or RDF tag rather than a vocabulary list, which may bein different in different languages, for example.

FIG. 2 is an example of an interest packet forwarding technique,according to an embodiment. As illustrated, a search interest packet isreceived at an ICN node and matched to similar content in a localcontent store via an inverted index (operation 205). As illustrated, thesearch index packet indicates that it is requesting N results to thesearch.

The node then determines whether it found N results that were within asimilarity score threshold specified in the search interest packet(decision 210). If yes, the node retrieves and aggregates the contentfrom the content store (operation 215) and sends it back to the searchinterest packet author.

If the content store does not have enough matches (decision 210), thenode creates a new interest packet with a search for the remainingsearch results (e.g., referred to as R above with respect to FIG. 1)(operation 220). A PIT entry is created to link this new interest to theoriginal search interest (operation 225). Also, the inverted index forthe FIB may be updated (e.g., entries are added, removed, changed, etc.)and consulted (operation 230) to determine a list of semanticallymatched FIB prefixes. The FIB is then consulted (operation 235) todetermine the next hops for these prefixes. The new interest packet isdropped if no entries are found in the FIB corresponding to theseprefixes (operation 235). Next hop information is then passed to theforwarding strategy engine and the new interest packet is routed toother nodes that may be able provide content to complete the searchrequest (operation 240).

FIG. 3 illustrates an example of a network topology for distributedsearching, according to an embodiment. Within the context of theprevious discussions related to FIGS. 1 and 2, the operations describedabove may be performed by the first node, NODE 1, and the consumer isthe author of the search interest packet. Note that the nodes may havecorresponding inverted indices to a FIB or to a content store,respectively identified as ICS or IFIB in FIG. 3.

In this topology, the consumer uses its IFIB to contact NODE 1 with thesearch interest packet. NODE 1 may then satisfy some elements from itsown content store (determined via the ICS), and propagate a new searchinterest to NODE 2 to complete the search request, and so on asdescribed above.

FIGS. 4A-4B illustrate an example of a message flow during execution ofa distributed search, according to an embodiment. This message flow isan illustration of that described above with respect to FIG. 1. In thisexample, the search interest includes the index_only parameter.Accordingly, the matched entries are returned as a manifest thatcontains links or actual names of the content that meets the searchcriteria. Here, meeting the search criteria is achieved when theelements of the search criteria are satisfied. Thus, if the searchcriteria call for an exact match, then meeting the search criteriainvolves an exact match. Similarly, if the search criteria define aclose match (e.g., within a threshold number of terms, synonyms, etc.),then the criteria are met when such a match is found. Meeting the searchcriteria produces results that is like returning a set of hyperlinks ina web search engine. Once a requesting device has one or more manifestscontaining the content names for content that satisfies the search,separate interest packets may be sent using the content's given name inthe more traditional ICN manner.

FIG. 5 is an example of a method for ICN distributed search withapproximate cache and forwarding information lookup, according to anembodiment. The operations of the method 500 are implemented incomputing hardware, such as that described in FIG. 6 or 7 (e.g.,processing circuitry).

At operation 505, a search interest packet is received (e.g., at an ICNnode). Here, the search interest packet includes search criteria and asignal indicating that it is a search interest packet. In an example,the signal is a prefix to the name, such as “/search” or “?” amongothers. Such a prefix enables the node to quickly ascertain that theinterest packet is a search interest packet.

The search criteria may include a query and one or more parameters. Theparameters may specify, for example, a minimum number of results toreturn, whether the results should be content or a manifest that liststhe content, how similar content should be to be considered a match forthe query, etc.

At operation 510, a search for content that meets the search criteria isperformed. Here, the search includes searching the content store of thenode (e.g., a local content store). In an example, when the searchcriteria include a similarity threshold, content meets the searchcriteria when a similarity score between the content and the query inthe search interest meets the similarity threshold. This enables thesearch interest author to expand or contract the search results.

In an example, the local content store search uses an inverted index ofthe content store to match the search criteria to elements within thecontent store. In an example, the inverted index includes key words,phrases, or data segments as keys and a list of content or portionswithin content that correspond to a key.

In an example, when the search criteria include a minimum number ofresults and when fewer than the minimum number of results are found inthe content store, the search includes creating a subsequent searchinterest and forwarding the subsequent search interest. Here, thesubsequent search interest may include a modification to parameters ofthe search interest to acquire additional search results to meet theminimum number of results. In an example, forwarding the subsequentsearch interest includes adding an entry in the pending interest table(PIT) to match the subsequent search interest to the search interest.This entry may be later used to aggregate the local search results withthe additional search results.

In an example, an inverted index of a FIB for the node is used toidentify likely forwarding routes to complete the search. In an example,the subsequent interest includes a maximum search result that is afraction of remaining search results to meet the minimum number ofresults based on the FIB. This helps to minimize wasted networkresources by assuming that each of the recipients will likely returnsome portion of the needed additional content items and avoiding havingto discard results in excess of the minimum number of search resultsspecified in the search interest packet.

At operation 515, a data packet that includes results of the search istransmitted toward the author of the search interest packet. In anexample, multiple data packets may be used to transmit the completesearch results. In an example, the node aggregates search results fromitself and other nodes before sending them back to the search interestauthor. In an example, the node sends search results back to the authoras they arrive.

FIGS. 6 and 7 below provide additional details of the components inFIG. 1. For example, FIG. 6 illustrates several details and variationsin ICNs. FIG. 7 illustrates several examples of computer hardware thatmay be used to implement any of the components illustrated in FIG. 1.

FIG. 6 illustrates an example ICN, according to an embodiment. ICNsoperate differently than traditional host-based (e.g., address-based)communication networks. ICN is an umbrella term for a networkingparadigm in which information itself is named and requested from thenetwork instead of hosts (e.g., machines that provide information). In ahost-based networking paradigm, such as used in the Internet protocol(IP), a device locates a host and requests content from the host. Thenetwork understands how to route (e.g., direct) packets based on theaddress specified in the packet. In contrast, ICN does not include arequest for a particular machine and does not use addresses. Instead, toget content, a device 605 (e.g., subscriber) requests named content fromthe network itself. The content request may be called an interest andtransmitted via an interest packet 630. As the interest packet traversesnetwork devices (e.g., network elements, routers, switches, hubs,etc.)—such as network elements 610, 615, and 620—a record of theinterest is kept, for example, in a pending interest table (PIT) at eachnetwork element. Thus, network element 610 maintains an entry in its PIT635 for the interest packet 630, network element 615 maintains the entryin its PIT, and network element 620 maintains the entry in its PIT.

When a device, such as publisher 640, that has content matching the namein the interest packet 630 is encountered, that device 640 may send adata packet 645 in response to the interest packet 630. Typically, thedata packet 645 is tracked back through the network to the source (e.g.,device 605) by following the traces of the interest packet 630 left inthe network element PITs. Thus, the PIT 635 at each network elementestablishes a trail back to the subscriber 605 for the data packet 645to follow.

Matching the named data in an ICN may follow several strategies.Generally, the data is named hierarchically, such as with a universalresource identifier (URI). For example, a video may be namedwww.somedomain.com or videos or v8675309. Here, the hierarchy may beseen as the publisher, “www.somedomain.com,” a sub-category, “videos,”and the canonical identification “v8675309.” As an interest 630traverses the ICN, ICN network elements will generally attempt to matchthe name to a greatest degree. Thus, if an ICN element has a cached itemor route for both “www.somedomain.com or videos” and “www.somedomain.comor videos or v8675309,” the ICN element will match the later for aninterest packet 630 specifying “www.somedomain.com or videos orv8675309.” In an example, an expression may be used in matching by theICN device. For example, the interest packet may specify“www.somedomain.com or videos or v8675*” where ‘*’ is a wildcard. Thus,any cached item or route that includes the data other than the wildcardwill be matched.

Item matching involves matching the interest 630 to data cached in theICN element. Thus, for example, if the data 645 named in the interest630 is cached in network element 615, then the network element 615 willreturn the data 645 to the subscriber 605 via the network element 610.However, if the data 645 is not cached at network element 615, thenetwork element 615 routes the interest 630 on (e.g., to network element620). To facilitate routing, the network elements may use a forwardinginformation base 625 (FIB) to match named data to an interface (e.g.,physical port) for the route. Thus, the FIB 625 operates much like arouting table on a traditional network device.

In an example, additional meta-data may be attached to the interestpacket 630, the cached data, or the route (e.g., in the FIB 625), toprovide an additional level of matching. For example, the data name maybe specified as “www.somedomain.com or videos or v8675309,” but alsoinclude a version number—or timestamp, time range, endorsement, etc. Inthis example, the interest packet 630 may specify the desired name, theversion number, or the version range. The matching may then locateroutes or cached data matching the name and perform the additionalcomparison of meta-data or the like to arrive at an ultimate decision asto whether data or a route matches the interest packet 630 forrespectively responding to the interest packet 630 with the data packet645 or forwarding the interest packet 630.

ICN has advantages over host-based networking because the data segmentsare individually named. This enables aggressive caching throughout thenetwork as a network element may provide a data packet 630 in responseto an interest 630 as easily as an original author 640. Accordingly, itis less likely that the same segment of the network will transmitduplicates of the same data requested by different devices.

Fine grained encryption is another feature of many ICN networks. Atypical data packet 645 includes a name for the data that matches thename in the interest packet 630. Further, the data packet 645 includesthe requested data and may include additional information to filtersimilarly named data (e.g., by creation time, expiration time, version,etc.). To address malicious entities providing false information underthe same name, the data packet 645 may also encrypt its contents with apublisher key or provide a cryptographic hash of the data and the name.Thus, knowing the key (e.g., from a certificate of an expected publisher640) enables the recipient to ascertain whether the data is from thatpublisher 640. This technique also facilitates the aggressive caching ofthe data packets 645 throughout the network because each data packet 645is self-contained and secure. In contrast, many host-based networks relyon encrypting a connection between two hosts to secure communications.This may increase latencies while connections are being established andprevents data caching by hiding the data from the network elements.

Example ICN networks include: content centric networking (CCN)—asspecified in the Internet Engineering Task Force (IETF) draftspecifications for CCNx 0.x and CCN 1.x; named data networking (NDN)—asspecified in the NDN technical report DND-0001; Data-Oriented NetworkArchitecture (DONA)—as presented at proceedings of the 2007 Associationfor Computing Machinery's (ACM) Special Interest Group on DataCommunications (SIGCOMM) conference on Applications, technologies,architectures, and protocols for computer communications; NamedFunctions Networking (NFN); 4WARD; Content Aware Searching, Retrievaland Streaming (COAST); Convergence of Fixed and Mobile BroadbandAccess/Aggregation Networks (COMBO); Content Mediator Architecture forContent-Aware Networks (COMET); CONVERGENCE; GreenICN; Network ofInformation (NetInf); IP Over ICN (POINT); Publish-Subscribe InternetRouting Paradigm (PSIRP); Publish Subscribe Internet Technology(PURSUIT); Scalable and Adaptive Internet Solutions (SAIL); Universal,Mobile-Centric and Opportunistic Communications Architecture (UMOBILE);among others.

FIG. 7 illustrates a block diagram of an example machine 700 upon whichany one or more of the techniques (e.g., methodologies) discussed hereinmay perform. Examples, as described herein, may include, or may operateby, logic or a number of components, or mechanisms in the machine 700.Circuitry (e.g., processing circuitry) is a collection of circuitsimplemented in tangible entities of the machine 700 that includehardware (e.g., simple circuits, gates, logic, etc.). Circuitrymembership may be flexible over time. Circuitries include members thatmay, alone or in combination, perform specified operations whenoperating. In an example, hardware of the circuitry may be immutablydesigned to carry out a specific operation (e.g., hardwired). In anexample, the hardware of the circuitry may include variably connectedphysical components (e.g., execution units, transistors, simplecircuits, etc.) including a machine readable medium physically modified(e.g., magnetically, electrically, moveable placement of invariantmassed particles, etc.) to encode instructions of the specificoperation. In connecting the physical components, the underlyingelectrical properties of a hardware constituent are changed, forexample, from an insulator to a conductor or vice versa. Theinstructions enable embedded hardware (e.g., the execution units or aloading mechanism) to create members of the circuitry in hardware viathe variable connections to carry out portions of the specific operationwhen in operation. Accordingly, in an example, the machine-readablemedium elements are part of the circuitry or are communicatively coupledto the other components of the circuitry when the device is operating.In an example, any of the physical components may be used in more thanone member of more than one circuitry. For example, under operation,execution units may be used in a first circuit of a first circuitry atone point in time and reused by a second circuit in the first circuitry,or by a third circuit in a second circuitry at a different time.Additional examples of these components with respect to the machine 700follow.

In alternative embodiments, the machine 700 may operate as a standalonedevice or may be connected (e.g., networked) to other machines. In anetworked deployment, the machine 700 may operate in the capacity of aserver machine, a client machine, or both in server-client networkenvironments. In an example, the machine 700 may act as a peer machinein peer-to-peer (P2P) (or other distributed) network environment. Themachine 700 may be a personal computer (PC), a tablet PC, a set-top box(STB), a personal digital assistant (PDA), a mobile telephone, a webappliance, a network router, switch or bridge, or any machine capable ofexecuting instructions (sequential or otherwise) that specify actions tobe taken by that machine. Further, while only a single machine isillustrated, the term “machine” shall also be taken to include anycollection of machines that individually or jointly execute a set (ormultiple sets) of instructions to perform any one or more of themethodologies discussed herein, such as cloud computing, software as aservice (SaaS), other computer cluster configurations.

The machine (e.g., computer system) 700 may include a hardware processor702 (e.g., a central processing unit (CPU), a graphics processing unit(GPU), a hardware processor core, or any combination thereof), a mainmemory 704, a static memory (e.g., memory or storage for firmware,microcode, a basic-input-output (BIOS), unified extensible firmwareinterface (UEFI), etc.) 706, and mass storage 708 (e.g., hard drive,tape drive, flash storage, or other block devices) some or all of whichmay communicate with each other via an interlink (e.g., bus) 730. Themachine 700 may further include a display unit 710, an alphanumericinput device 712 (e.g., a keyboard), and a user interface (UI)navigation device 714 (e.g., a mouse). In an example, the display unit710, input device 712 and UI navigation device 714 may be a touch screendisplay. The machine 700 may additionally include a storage device(e.g., drive unit) 708, a signal generation device 718 (e.g., aspeaker), a network interface device 720, and one or more sensors 716,such as a global positioning system (GPS) sensor, compass,accelerometer, or other sensor. The machine 700 may include an outputcontroller 728, such as a serial (e.g., universal serial bus (USB),parallel, or other wired or wireless (e.g., infrared (IR), near fieldcommunication (NFC), etc.) connection to communicate or control one ormore peripheral devices (e.g., a printer, card reader, etc.).

Registers of the processor 702, the main memory 704, the static memory706, or the mass storage 708 may be, or include, a machine readablemedium 722 on which is stored one or more sets of data structures orinstructions 724 (e.g., software) embodying or utilized by any one ormore of the techniques or functions described herein. The instructions724 may also reside, completely or at least partially, within any ofregisters of the processor 702, the main memory 704, the static memory706, or the mass storage 708 during execution thereof by the machine700. In an example, one or any combination of the hardware processor702, the main memory 704, the static memory 706, or the mass storage 708may constitute the machine readable media 722. While the machinereadable medium 722 is illustrated as a single medium, the term “machinereadable medium” may include a single medium or multiple media (e.g., acentralized or distributed database, and/or associated caches andservers) configured to store the one or more instructions 724.

The term “machine readable medium” may include any medium that iscapable of storing, encoding, or carrying instructions for execution bythe machine 700 and that cause the machine 700 to perform any one ormore of the techniques of the present disclosure, or that is capable ofstoring, encoding or carrying data structures used by or associated withsuch instructions. Non-limiting machine-readable medium examples mayinclude solid-state memories, optical media, magnetic media, and signals(e.g., radio frequency signals, other photon-based signals, soundsignals, etc.). In an example, a non-transitory machine-readable mediumcomprises a machine-readable medium with a plurality of particles havinginvariant (e.g., rest) mass, and thus are compositions of matter.Accordingly, non-transitory machine-readable media are machine readablemedia that do not include transitory propagating signals. Specificexamples of non-transitory machine-readable media may include:non-volatile memory, such as semiconductor memory devices (e.g.,Electrically Programmable Read-Only Memory (EPROM), ElectricallyErasable Programmable Read-Only Memory (EEPROM)) and flash memorydevices; magnetic disks, such as internal hard disks and removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

The instructions 724 may be further transmitted or received over acommunications network 726 using a transmission medium via the networkinterface device 720 utilizing any one of a number of transfer protocols(e.g., frame relay, internet protocol (IP), transmission controlprotocol (TCP), user datagram protocol (UDP), hypertext transferprotocol (HTTP), etc.). Example communication networks may include alocal area network (LAN), a wide area network (WAN), a packet datanetwork (e.g., the Internet), mobile telephone networks (e.g., cellularnetworks), Plain Old Telephone (POTS) networks, and wireless datanetworks (e.g., Institute of Electrical and Electronics Engineers (IEEE)802.11 family of standards known as Wi-Fi®, IEEE 802.16 family ofstandards known as WiMax®), IEEE 802.15.4 family of standards,peer-to-peer (P2P) networks, among others. In an example, the networkinterface device 720 may include one or more physical jacks (e.g.,Ethernet, coaxial, or phone jacks) or one or more antennas to connect tothe communications network 726. In an example, the network interfacedevice 720 may include a plurality of antennas to wirelessly communicateusing at least one of single-input multiple-output (SIMO),multiple-input multiple-output (MIMO), or multiple-input single-output(MISO) techniques. The term “transmission medium” shall be taken toinclude any intangible medium that is capable of storing, encoding orcarrying instructions for execution by the machine 700, and includesdigital or analog communications signals or other intangible medium tofacilitate communication of such software. A transmission medium is amachine readable medium.

ADDITIONAL NOTES & EXAMPLES

Example 1 is a device for an information centric network (ICN)distributed search with approximate cache and forwarding informationlookup, the device comprising: processing circuitry; and memoryincluding instructions that, when the device is operating, configure theprocessing circuitry to: receive a search interest packet, the searchinterest packet including search criteria and a signal indicating thatit is a search interest packet; perform a search for content that meetsthe search criteria, the search including searching a local contentstore; and transmit a data packet towards an author of the searchinterest packet, the data packet including results of the search.

In Example 2, the subject matter of Example 1, wherein, to search thelocal content store, the processing circuitry uses an inverted index ofthe content store to match the search criteria to elements within thecontent store.

In Example 3, the subject matter of Example 2, wherein the invertedindex includes key words, phrases, or data segments as keys and a listof content or portions within content that correspond to a key.

In Example 4, the subject matter of any of Examples 1-3, wherein thesearch criteria include a minimum number of results, wherein fewer thanthe minimum number of results are found in the content store, andwherein, to perform the search for the content, the instructionsconfigure the processing circuitry to: create a subsequent searchinterest, the subsequent search interest including modification toparameters of the search interest to acquire additional search resultsto meet the minimum number of results; and forward the subsequent searchinterest.

In Example 5, the subject matter of Example 4, wherein, to forward thesubsequent search interest, the processing circuitry adds an entry inthe pending interest table (PIT) to match the subsequent search interestto the search interest.

In Example 6, the subject matter of any of Examples 4-5, wherein aninverted index of a forwarding interest base (FIB) is used to identifylikely forward routes to complete the search.

In Example 7, the subject matter of Example 6, wherein the subsequentinterest includes a maximum search result that is a fraction ofremaining search results to meet the minimum number of results based onthe FIB.

In Example 8, the subject matter of any of Examples 4-7, wherein acluster head creates the subsequent search interest packet.

In Example 9, the subject matter of Example 8, wherein the cluster headaggregates responses to the subsequent search interest packet.

In Example 10, the subject matter of any of Examples 1-9, wherein thesearch criteria include a similarity threshold, content meeting thesearch criteria when a similarity score between the content and a queryin the search interest meets the similarity threshold.

In Example 11, the subject matter of any of Examples 1-10, wherein thesearch interest packet includes a stop-list field that indicates contentthat matches the search criteria but should not be returned.

Example 12 is a method for an information centric network (ICN)distributed search with approximate cache and forwarding informationlookup, the method comprising: receiving a search interest packet, thesearch interest packet including search criteria and a signal indicatingthat it is a search interest packet; performing a search for contentthat meets the search criteria, the search including searching a localcontent store; and transmitting a data packet towards an author of thesearch interest packet, the data packet including results of the search.

In Example 13, the subject matter of Example 12, wherein searching thelocal content store includes using an inverted index of the contentstore to match the search criteria to elements within the content store.

In Example 14, the subject matter of Example 13, wherein the invertedindex includes key words, phrases, or data segments as keys and a listof content or portions within content that correspond to a key.

In Example 15, the subject matter of any of Examples 12-14, wherein thesearch criteria include a minimum number of results, wherein fewer thanthe minimum number of results are found in the content store, andwherein performing the search for the content includes: creating asubsequent search interest, the subsequent search interest includingmodification to parameters of the search interest to acquire additionalsearch results to meet the minimum number of results; and forwarding thesubsequent search interest.

In Example 16, the subject matter of Example 15, wherein forwarding thesubsequent search interest includes adding an entry in the pendinginterest table (PIT) to match the subsequent search interest to thesearch interest.

In Example 17, the subject matter of any of Examples 15-16, wherein aninverted index of a forwarding interest base (FIB) is used to identifylikely forward routes to complete the search.

In Example 18, the subject matter of Example 17, wherein the subsequentinterest includes a maximum search result that is a fraction ofremaining search results to meet the minimum number of results based onthe FIB.

In Example 19, the subject matter of any of Examples 15-18, wherein acluster head creates the subsequent search interest packet.

In Example 20, the subject matter of Example 19, wherein the clusterhead aggregates responses to the subsequent search interest packet.

In Example 21, the subject matter of any of Examples 12-20, wherein thesearch criteria include a similarity threshold, content meeting thesearch criteria when a similarity score between the content and a queryin the search interest meets the similarity threshold.

In Example 22, the subject matter of any of Examples 12-21, wherein thesearch interest packet includes a stop-list field that indicates contentthat matches the search criteria but should not be returned.

Example 23 is a at least one machine-readable medium includinginstructions for an information centric network (ICN) distributed searchwith approximate cache and forwarding information lookup, theinstructions, when executed by processing circuitry, cause theprocessing circuitry to perform operations comprising: receiving asearch interest packet, the search interest packet including searchcriteria and a signal indicating that it is a search interest packet;performing a search for content that meets the search criteria, thesearch including searching a local content store; and transmitting adata packet towards an author of the search interest packet, the datapacket including results of the search.

In Example 24, the subject matter of Example 23, wherein searching thelocal content store includes using an inverted index of the contentstore to match the search criteria to elements within the content store.

In Example 25, the subject matter of Example 24, wherein the invertedindex includes key words, phrases, or data segments as keys and a listof content or portions within content that correspond to a key.

In Example 26, the subject matter of any of Examples 23-25, wherein thesearch criteria include a minimum number of results, wherein fewer thanthe minimum number of results are found in the content store, andwherein performing the search for the content includes: creating asubsequent search interest, the subsequent search interest includingmodification to parameters of the search interest to acquire additionalsearch results to meet the minimum number of results; and forwarding thesubsequent search interest.

In Example 27, the subject matter of Example 26, wherein forwarding thesubsequent search interest includes adding an entry in the pendinginterest table (PIT) to match the subsequent search interest to thesearch interest.

In Example 28, the subject matter of any of Examples 26-27, wherein aninverted index of a forwarding interest base (FIB) is used to identifylikely forward routes to complete the search.

In Example 29, the subject matter of Example 28, wherein the subsequentinterest includes a maximum search result that is a fraction ofremaining search results to meet the minimum number of results based onthe FIB.

In Example 30, the subject matter of any of Examples 26-29, wherein acluster head creates the subsequent search interest packet.

In Example 31, the subject matter of Example 30, wherein the clusterhead aggregates responses to the subsequent search interest packet.

In Example 32, the subject matter of any of Examples 23-31, wherein thesearch criteria include a similarity threshold, content meeting thesearch criteria when a similarity score between the content and a queryin the search interest meets the similarity threshold.

In Example 33, the subject matter of any of Examples 23-32, wherein thesearch interest packet includes a stop-list field that indicates contentthat matches the search criteria but should not be returned.

Example 34 is a system for an information centric network (ICN)distributed search with approximate cache and forwarding informationlookup, the system comprising: means for receiving a search interestpacket, the search interest packet including search criteria and asignal indicating that it is a search interest packet; means forperforming a search for content that meets the search criteria, thesearch including searching a local content store; and means fortransmitting a data packet towards an author of the search interestpacket, the data packet including results of the search.

In Example 35, the subject matter of Example 34, wherein the means forsearching the local content store include means for using an invertedindex of the content store to match the search criteria to elementswithin the content store.

In Example 36, the subject matter of Example 35, wherein the invertedindex includes key words, phrases, or data segments as keys and a listof content or portions within content that correspond to a key.

In Example 37, the subject matter of any of Examples 34-36, wherein thesearch criteria include a minimum number of results, wherein fewer thanthe minimum number of results are found in the content store, andwherein the means for performing the search for the content include:means for creating a subsequent search interest, the subsequent searchinterest including modification to parameters of the search interest toacquire additional search results to meet the minimum number of results;and means for forwarding the subsequent search interest.

In Example 38, the subject matter of Example 37, wherein the means forforwarding the subsequent search interest include means for adding anentry in the pending interest table (PIT) to match the subsequent searchinterest to the search interest.

In Example 39, the subject matter of any of Examples 37-38, wherein aninverted index of a forwarding interest base (FIB) is used to identifylikely forward routes to complete the search.

In Example 40, the subject matter of Example 39, wherein the subsequentinterest includes a maximum search result that is a fraction ofremaining search results to meet the minimum number of results based onthe FIB.

In Example 41, the subject matter of any of Examples 37-40, wherein acluster head creates the subsequent search interest packet.

In Example 42, the subject matter of Example 41, wherein the clusterhead aggregates responses to the subsequent search interest packet.

In Example 43, the subject matter of any of Examples 34-42, wherein thesearch criteria include a similarity threshold, content meeting thesearch criteria when a similarity score between the content and a queryin the search interest meets the similarity threshold.

In Example 44, the subject matter of any of Examples 34-43, wherein thesearch interest packet includes a stop-list field that indicates contentthat matches the search criteria but should not be returned.

Example 45 is at least one machine-readable medium includinginstructions that, when executed by processing circuitry, cause theprocessing circuitry to perform operations to implement of any ofExamples 1-44.

Example 46 is an apparatus comprising means to implement of any ofExamples 1-44.

Example 47 is a system to implement of any of Examples 1-44.

Example 48 is a method to implement of any of Examples 1-44.

The above detailed description includes references to the accompanyingdrawings, which form a part of the detailed description. The drawingsshow, by way of illustration, specific embodiments that may bepracticed. These embodiments are also referred to herein as “examples.”Such examples may include elements in addition to those shown ordescribed. However, the present inventors also contemplate examples inwhich only those elements shown or described are provided. Moreover, thepresent inventors also contemplate examples using any combination orpermutation of those elements shown or described (or one or more aspectsthereof), either with respect to a particular example (or one or moreaspects thereof), or with respect to other examples (or one or moreaspects thereof) shown or described herein.

All publications, patents, and patent documents referred to in thisdocument are incorporated by reference herein in their entirety, asthough individually incorporated by reference. In the event ofinconsistent usages between this document and those documents soincorporated by reference, the usage in the incorporated reference(s)should be considered supplementary to that of this document; forirreconcilable inconsistencies, the usage in this document controls.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended, that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim are still deemed to fall within thescope of that claim. Moreover, in the following claims, the terms“first,” “second,” and “third,” etc. are used merely as labels, and arenot intended to impose numerical requirements on their objects.

The above description is intended to be illustrative, and notrestrictive. For example, the above-described examples (or one or moreaspects thereof) may be used in combination with each other. Otherembodiments may be used, such as by one of ordinary skill in the artupon reviewing the above description. The Abstract is to allow thereader to quickly ascertain the nature of the technical disclosure andis submitted with the understanding that it will not be used tointerpret or limit the scope or meaning of the claims. Also, in theabove Detailed Description, various features may be grouped together tostreamline the disclosure. This should not be interpreted as intendingthat an unclaimed disclosed feature is essential to any claim. Rather,inventive subject matter may lie in less than all features of aparticular disclosed embodiment. Thus, the following claims are herebyincorporated into the Detailed Description, with each claim standing onits own as a separate embodiment. The scope of the embodiments should bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

What is claimed is:
 1. A device for an information centric network (ICN) distributed search with approximate cache and forwarding information lookup, the device comprising: processing circuitry; and memory including instructions that, when the device is operating, configure the processing circuitry to: receive a search interest packet, the search interest packet including search criteria and a signal indicating that it is a search interest packet; perform a search for content that meets the search criteria, the search including searching a local content store; and transmit a data packet towards an author of the search interest packet, the data packet including results of the search.
 2. The device of claim 1, wherein, to search the local content store, the processing circuitry uses an inverted index of the content store to match the search criteria to elements within the content store.
 3. The device of claim 1, wherein the search criteria include a minimum number of results, wherein fewer than the minimum number of results are found in the content store, and wherein, to perform the search for the content, the instructions configure the processing circuitry to: create a subsequent search interest, the subsequent search interest including modification to parameters of the search interest to acquire additional search results to meet the minimum number of results; and forward the subsequent search interest.
 4. The device of claim 3, wherein, to forward the subsequent search interest, the processing circuitry adds an entry in the pending interest table (PIT) to match the subsequent search interest to the search interest.
 5. The device of claim 3, wherein an inverted index of a forwarding interest base (FIB) is used to identify likely forward routes to complete the search.
 6. The device of claim 5, wherein the subsequent interest includes a maximum search result that is a fraction of remaining search results to meet the minimum number of results based on the FIB.
 7. The device of claim 1, wherein the search criteria include a similarity threshold, content meeting the search criteria when a similarity score between the content and a query in the search interest meets the similarity threshold.
 8. The device of claim 1, wherein the search interest packet includes a stop-list field that indicates content that matches the search criteria but should not be returned.
 9. A method for an information centric network (ICN) distributed search with approximate cache and forwarding information lookup, the method comprising: receiving a search interest packet, the search interest packet including search criteria and a signal indicating that it is a search interest packet; performing a search for content that meets the search criteria, the search including searching a local content store; and transmitting a data packet towards an author of the search interest packet, the data packet including results of the search.
 10. The method of claim 9, wherein searching the local content store includes using an inverted index of the content store to match the search criteria to elements within the content store.
 11. The method of claim 9, wherein the search criteria include a minimum number of results, wherein fewer than the minimum number of results are found in the content store, and wherein performing the search for the content includes: creating a subsequent search interest, the subsequent search interest including modification to parameters of the search interest to acquire additional search results to meet the minimum number of results; and forwarding the subsequent search interest.
 12. The method of claim 11, wherein forwarding the subsequent search interest includes adding an entry in the pending interest table (PIT) to match the subsequent search interest to the search interest.
 13. The method of claim 11, wherein an inverted index of a forwarding interest base (FIB) is used to identify likely forward routes to complete the search.
 14. The method of claim 13, wherein the subsequent interest includes a maximum search result that is a fraction of remaining search results to meet the minimum number of results based on the FIB.
 15. The method of claim 9, wherein the search criteria include a similarity threshold, content meeting the search criteria when a similarity score between the content and a query in the search interest meets the similarity threshold.
 16. The method of claim 9, wherein the search interest packet includes a stop-list field that indicates content that matches the search criteria but should not be returned.
 17. At least one machine-readable medium including instructions for an information centric network (ICN) distributed search with approximate cache and forwarding information lookup, the instructions, when executed by processing circuitry, cause the processing circuitry to perform operations comprising: receiving a search interest packet, the search interest packet including search criteria and a signal indicating that it is a search interest packet; performing a search for content that meets the search criteria, the search including searching a local content store; and transmitting a data packet towards an author of the search interest packet, the data packet including results of the search.
 18. The at least one machine-readable medium of claim 17, wherein searching the local content store includes using an inverted index of the content store to match the search criteria to elements within the content store.
 19. The at least one machine-readable medium of claim 17, wherein the search criteria include a minimum number of results, wherein fewer than the minimum number of results are found in the content store, and wherein performing the search for the content includes: creating a subsequent search interest, the subsequent search interest including modification to parameters of the search interest to acquire additional search results to meet the minimum number of results; and forwarding the subsequent search interest.
 20. The at least one machine-readable medium of claim 19, wherein forwarding the subsequent search interest includes adding an entry in the pending interest table (PIT) to match the subsequent search interest to the search interest.
 21. The at least one machine-readable medium of claim 19, wherein an inverted index of a forwarding interest base (FIB) is used to identify likely forward routes to complete the search.
 22. The at least one machine-readable medium of claim 21, wherein the subsequent interest includes a maximum search result that is a fraction of remaining search results to meet the minimum number of results based on the FIB.
 23. The at least one machine-readable medium of claim 17, wherein the search criteria include a similarity threshold, content meeting the search criteria when a similarity score between the content and a query in the search interest meets the similarity threshold.
 24. The at least one machine-readable medium of claim 17, wherein the search interest packet includes a stop-list field that indicates content that matches the search criteria but should not be returned. 